We further verify the usefulness of computing second-order derivatives in practical problem instances by applying the Newton's Method to tuning a multi-class SVM model [1]} on the Fashion-MNIST [2]} dataset for best performance on the test set. We reformulate the constraints as log-barrier penaltyThe log-barrier is defined for a constraint \(x \le 0\)  as \(-\log \left(-\alpha x\right) / \alpha \)  for a tunable refinement constant \(\alpha \) . to ensure smoothness, with the refinement value of \(\alpha = 10^2\) . We solve the resulting problem with the Mosek optimizer.www.mosek.com
